{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 代码不作为评判标准，如果运⾏正确，则认为代码没有错误。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.13.1'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.examples.tutorials.mnist import input_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting .\\train-images-idx3-ubyte.gz\n",
      "Extracting .\\train-labels-idx1-ubyte.gz\n",
      "Extracting .\\t10k-images-idx3-ubyte.gz\n",
      "Extracting .\\t10k-labels-idx1-ubyte.gz\n",
      "Extracting .\\train-images-idx3-ubyte.gz\n",
      "Extracting .\\train-labels-idx1-ubyte.gz\n",
      "Extracting .\\t10k-images-idx3-ubyte.gz\n",
      "Extracting .\\t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "mnist = input_data.read_data_sets('.', one_hot=True)\n",
    "mnist2 = input_data.read_data_sets('.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
      " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]]\n",
      "[7 3]\n"
     ]
    }
   ],
   "source": [
    "print(mnist.train.labels[:2])\n",
    "print(mnist2.train.labels[:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#每个批次的大小\n",
    "#以矩阵的形式放进去\n",
    "batch_size = 100\n",
    "#计算一共有多少个批次\n",
    "n_batch = mnist.train.num_examples // batch_size\n",
    " \n",
    " \n",
    "#定义三个placeholder\n",
    "#28 x 28 = 784\n",
    "x = tf.placeholder(tf.float32, [None, 784])\n",
    "y = tf.placeholder(tf.float32, [None, 10])\n",
    "keep_prob = tf.placeholder(tf.float32)\n",
    "#学习率\n",
    "lr = tf.Variable(0.001, dtype=tf.float32)\n",
    " \n",
    " \n",
    "#创建一个的神经网络\n",
    "#输入层784，隐藏层一500，隐藏层二300，输出层10个神经元\n",
    "#隐藏层\n",
    "W1 = tf.Variable(tf.truncated_normal([784, 500], stddev=0.1))\n",
    "b1 = tf.Variable(tf.zeros([500]) + 0.1)\n",
    "L1 = tf.nn.tanh(tf.matmul(x, W1) + b1)\n",
    "L1_drop = tf.nn.dropout(L1,keep_prob)\n",
    " \n",
    " \n",
    "W2 = tf.Variable(tf.truncated_normal([500, 300], stddev=0.1))\n",
    "b2 = tf.Variable(tf.zeros([300]) + 0.1)\n",
    "L2 = tf.nn.tanh(tf.matmul(L1_drop, W2) + b2)\n",
    "L2_drop = tf.nn.dropout(L2,keep_prob)\n",
    " \n",
    " \n",
    " \n",
    "W3 = tf.Variable(tf.truncated_normal([300, 10], stddev=0.1))\n",
    "b3 = tf.Variable(tf.zeros([10]) + 0.1)\n",
    "prediction = tf.nn.softmax(tf.matmul(L2_drop, W3) + b3)\n",
    " \n",
    "#交叉熵代价函数\n",
    "loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=prediction))\n",
    " \n",
    "#训练\n",
    "train_step = tf.train.AdamOptimizer(lr).minimize(loss)\n",
    " \n",
    "#初始化变量\n",
    "init = tf.global_variables_initializer()\n",
    " \n",
    " \n",
    " \n",
    "#结果存放在一个布尔型列表中\n",
    "#tf.argmax(y, 1)与tf.argmax(prediction, 1)相同返回True,不同则返回False\n",
    "#argmax返回一维张量中最大的值所在的位置\n",
    "correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(prediction, 1))\n",
    " \n",
    "#求准确率\n",
    "#tf.cast(correct_prediction, tf.float32) 将布尔型转换为浮点型\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "grap = tf.get_default_graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.framework.ops.Graph at 0x15fe4f0e898>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
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       "  name: \"Mean_5\"\n",
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       "  input: \"Const_5\"\n",
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       "versions {\n",
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      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grap.as_graph_def()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter0, Testing Accuracy0.958\n",
      "Iter1, Testing Accuracy0.966\n",
      "Iter2, Testing Accuracy0.969\n",
      "Iter3, Testing Accuracy0.9746\n",
      "Iter4, Testing Accuracy0.9774\n",
      "Iter5, Testing Accuracy0.9748\n",
      "Iter6, Testing Accuracy0.9764\n",
      "Iter7, Testing Accuracy0.9806\n",
      "Iter8, Testing Accuracy0.9776\n",
      "Iter9, Testing Accuracy0.9798\n",
      "Iter10, Testing Accuracy0.98\n",
      "Iter11, Testing Accuracy0.9814\n",
      "Iter12, Testing Accuracy0.9818\n",
      "Iter13, Testing Accuracy0.983\n",
      "Iter14, Testing Accuracy0.9808\n",
      "Iter15, Testing Accuracy0.9826\n",
      "Iter16, Testing Accuracy0.9842\n",
      "Iter17, Testing Accuracy0.983\n",
      "Iter18, Testing Accuracy0.983\n",
      "Iter19, Testing Accuracy0.982\n",
      "Iter20, Testing Accuracy0.9832\n",
      "Iter21, Testing Accuracy0.9838\n",
      "Iter22, Testing Accuracy0.9812\n",
      "Iter23, Testing Accuracy0.984\n",
      "Iter24, Testing Accuracy0.9838\n",
      "Iter25, Testing Accuracy0.9854\n",
      "Iter26, Testing Accuracy0.985\n",
      "Iter27, Testing Accuracy0.984\n",
      "Iter28, Testing Accuracy0.9848\n",
      "Iter29, Testing Accuracy0.9848\n",
      "Iter30, Testing Accuracy0.9852\n",
      "Iter31, Testing Accuracy0.9834\n",
      "Iter32, Testing Accuracy0.9844\n",
      "Iter33, Testing Accuracy0.9848\n",
      "Iter34, Testing Accuracy0.9838\n",
      "Iter35, Testing Accuracy0.9846\n",
      "Iter36, Testing Accuracy0.986\n",
      "Iter37, Testing Accuracy0.9848\n",
      "Iter38, Testing Accuracy0.9842\n",
      "Iter39, Testing Accuracy0.9856\n",
      "Iter40, Testing Accuracy0.9846\n",
      "Iter41, Testing Accuracy0.9844\n",
      "Iter42, Testing Accuracy0.9852\n",
      "Iter43, Testing Accuracy0.9852\n",
      "Iter44, Testing Accuracy0.9858\n",
      "Iter45, Testing Accuracy0.9862\n",
      "Iter46, Testing Accuracy0.9852\n",
      "Iter47, Testing Accuracy0.9852\n",
      "Iter48, Testing Accuracy0.9852\n",
      "Iter49, Testing Accuracy0.9846\n",
      "Iter50, Testing Accuracy0.985\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    #总共51个周期\n",
    "    for epoch in range(51):\n",
    "        #刚开始学习率比较大，后来慢慢变小\n",
    "        sess.run(tf.assign(lr, 0.001 * (0.95 ** epoch)))\n",
    "        #总共n_batch个批次\n",
    "        for batch in range(n_batch):\n",
    "            #获得一个批次\n",
    "            batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n",
    "            sess.run(train_step, feed_dict={x:batch_xs, y:batch_ys, keep_prob:1.0})\n",
    "        \n",
    "        learning_rate = sess.run(lr)\n",
    "        #训练完一个周期后测试数据准确率\n",
    "        acc = sess.run(accuracy, feed_dict={x:mnist.validation.images, y:mnist.validation.labels, keep_prob:1.0})\n",
    "        \n",
    "        print(\"Iter\" + str(epoch) + \", Testing Accuracy\" + str(acc))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 没有明显报错的正常的log输出 ，log中的模型准确率达到98以上 60分。 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 如何修改隐层数量，修改后会起到什么样的效果10分。 \n",
    "添加两个隐层，修改后模型更全面，在validation中准确率大大提升。\n",
    "当训练集确定之后，输入层结点数和输出层结点数随之而确定，首先遇到的一个十分重要而又困难的问题是如何优化隐层结点数和隐层数。实验表明，如果隐层结点数过少，网络不能具有必要的学习能力和信息处理能力。反之，若过多，不仅会大大增加网络结构的复杂性（这一点对硬件实现的网络尤其重要），网络在学习过程中更易陷入局部极小点，而且会使网络的学习速度变得很慢。隐层结点数的选择问题一直受到神经网络研究工作者的高度重视。\n",
    "\n",
    "方法1： \n",
    "fangfaGorman指出隐层结点数s与模式数N的关系是：s＝log2N；\n",
    "\n",
    "方法二： \n",
    "Kolmogorov定理表明，隐层结点数s＝2n＋1（n为输入层结点数）；\n",
    "\n",
    "方法三： \n",
    "s＝sqrt（0.43mn＋0.12nn＋2.54m＋0.77n＋0.35）＋0.51 \n",
    "（m是输入层的个数，n是输出层的个数）。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 如调整何神经元个数，起到了什么样的效果10分。 \n",
    "当训练集确定之后，输入层结点数和输出层结点数随之而确定，首先遇到的一个十分重要而又困难的问题是如何优化隐层结点数和隐层数。实验表明，如果隐层结点数过少，网络不能具有必要的学习能力和信息处理能力。反之，若过多，不仅会大大增加网络结构的复杂性（这一点对硬件实现的网络尤其重要），网络在学习过程中更易陷入局部极小点，而且会使网络的学习速度变得很慢。隐层结点数的选择问题一直受到神经网络研究工作者的高度重视。\n",
    "\n",
    "方法1： \n",
    "fangfaGorman指出隐层结点数s与模式数N的关系是：s＝log2N；\n",
    "\n",
    "方法二： \n",
    "Kolmogorov定理表明，隐层结点数s＝2n＋1（n为输入层结点数）；\n",
    "\n",
    "方法三： \n",
    "s＝sqrt（0.43mn＋0.12nn＋2.54m＋0.77n＋0.35）＋0.51 \n",
    "（m是输入层的个数，n是输出层的个数）。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 如何在模型中添加L1/L2正则化，正则化起什么作⽤10分。  \n",
    "如何添加正则项：更改损失函数为增加L1/L2的正则项，然后对正则的参数进行调优。\n",
    "正则化的作用：控制模型的复杂度。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 使⽤不同的初始化⽅式对模型有什么影响10分。\n",
    "在不同的参数初始化⽅法下使模型更容易训练。这是由于当sigmoid激活函数输出极接近0或1时，这些区域的梯度⼏乎为0，从而造成反向传播⽆法继续更新部分模型参数；而ReLU激活函数在正区间的梯度恒为1。因此，若模型参数初始化不当，sigmoid函数可能在正区间得到⼏乎为0的梯度，从而令模型⽆法得到有效训练。\n"
   ]
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   "source": []
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   "execution_count": null,
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